This document provides a course syllabus for the Adaptive Signal Processing course offered by Blekinge Institute of Technology. The 7.5 ECTS credit course aims to provide students with background knowledge and skills in adaptive and optimal systems as applied to signal processing problems. Key topics covered include stochastic signals, optimal signal processing using estimation and filtering, and adaptive signal processing using algorithms like the LMS filter. Assessment involves a written exam, laboratory work, and written assignments, with the final grade based on exam performance. The course aims to allow students to design Wiener filters and apply adaptive systems to appropriate problems.
Broadening the scope of a Maths module for student Technology teachersUofGlasgowLTU
In this paper we will discuss the use of Moodle 2.4 Activities to enhance student learning in an undergraduate first year mathematics module. We begin by setting out the reasons for redesigning an existing course by using Moodle 2.4, and our reasons for selecting the activities that we added to the course. We present examples of student engagement with the course and end with time for questions from the audience.
Over the last three sessions, we have redeveloped a Maths module for student Technology teachers to provide an experience that is more relevant to their intended career. The most recent version of this was written this year by using Moodle 2.4, forums, wikis, the “External Tool” facility and Mahara.
Previously, the module was essentially a revision and levelling-up course, which was intended to ensure that students’ mathematical capability was sufficient to cope with the rest of their course. Students were required to complete ten tests covering topics from numeracy to differentiation and complex numbers, and attendance was mandatory only until they had done so. This led to a “race to finish” attitude, which had the more able students leaving the class early in the second semester and the less able battling on with completing the tests as their only goal. Understandably, engagement was minimal, the module was regarded as a chore and its relevance to the remainder of their course was poorly understood.
Realising that the students need to learn to take the teacher’s viewpoint, we introduced a “topics wiki” in which groups of students collaborate to provide additional explanations and resources around the course content. The efforts so far are very worthwhile and will be of use to those with less experience of Maths and to future students. Students are encouraged to discuss the resources during class time, and beyond. Some of the more able students are helping their classmates already, and we are actively encouraging this. We are also encouraging students to use these group wikis to build personal e-portfolios using Mahara, and this will be reinforced next semester when students participate in group projects.
Students are more engaged this year than in previous years, and we believe that this is because we have made better use of the functionality of Moodle, and are scaffolding student learning as they progress through the course.
Competition Gurukul Provides the best Coaching for the Polytechnic/LEET Entra...COMPETITION GURUKUL
We Provide the best Coaching through the
well Qualified and Specialized Faculty for Polytechnic/LEET Entrance Exams.
Our main focus is on concept Clearing with tricky approach to get Success in the Entrance Exams with the little efforts of the
candidates & Maximum efforts of our faculty.
The Moodle Gradebook as a tool inducing regular revisions in students' learning process Piotr Jaworski
Presented at Moodlemoot Edinburgh 2014
www.moodlemoot.ie
Broadening the scope of a Maths module for student Technology teachersUofGlasgowLTU
In this paper we will discuss the use of Moodle 2.4 Activities to enhance student learning in an undergraduate first year mathematics module. We begin by setting out the reasons for redesigning an existing course by using Moodle 2.4, and our reasons for selecting the activities that we added to the course. We present examples of student engagement with the course and end with time for questions from the audience.
Over the last three sessions, we have redeveloped a Maths module for student Technology teachers to provide an experience that is more relevant to their intended career. The most recent version of this was written this year by using Moodle 2.4, forums, wikis, the “External Tool” facility and Mahara.
Previously, the module was essentially a revision and levelling-up course, which was intended to ensure that students’ mathematical capability was sufficient to cope with the rest of their course. Students were required to complete ten tests covering topics from numeracy to differentiation and complex numbers, and attendance was mandatory only until they had done so. This led to a “race to finish” attitude, which had the more able students leaving the class early in the second semester and the less able battling on with completing the tests as their only goal. Understandably, engagement was minimal, the module was regarded as a chore and its relevance to the remainder of their course was poorly understood.
Realising that the students need to learn to take the teacher’s viewpoint, we introduced a “topics wiki” in which groups of students collaborate to provide additional explanations and resources around the course content. The efforts so far are very worthwhile and will be of use to those with less experience of Maths and to future students. Students are encouraged to discuss the resources during class time, and beyond. Some of the more able students are helping their classmates already, and we are actively encouraging this. We are also encouraging students to use these group wikis to build personal e-portfolios using Mahara, and this will be reinforced next semester when students participate in group projects.
Students are more engaged this year than in previous years, and we believe that this is because we have made better use of the functionality of Moodle, and are scaffolding student learning as they progress through the course.
Competition Gurukul Provides the best Coaching for the Polytechnic/LEET Entra...COMPETITION GURUKUL
We Provide the best Coaching through the
well Qualified and Specialized Faculty for Polytechnic/LEET Entrance Exams.
Our main focus is on concept Clearing with tricky approach to get Success in the Entrance Exams with the little efforts of the
candidates & Maximum efforts of our faculty.
The Moodle Gradebook as a tool inducing regular revisions in students' learning process Piotr Jaworski
Presented at Moodlemoot Edinburgh 2014
www.moodlemoot.ie
At its worst, science fiction reads like the desperate escapism of lonely boys whose only connection to high school sport was having their head slammed into a locker each morning by members of the football team. It reaches for wild, fantastic landscapes where heroic spacemen wearing glasses and a retainer zap cowering underlings in helmets and shoulder pads with a ray gun. At the other extreme, however, is science fiction that reaches into the human condition to expose universal truths. Along the way, it has an uncanny knack of predicting the future…
1. Blekinge Institute of Technology
Department of Applied Signal Processing
COURSE SYLLABUS
Adaptiv signalbehandling
Adaptive Signal Processing
7,5 ECTS credit points (7,5 högskolepoäng)
Course code: ET2542
Educational level: Advanced level
Course level: A1N
Field of education: Technology
Subject group: Electrical Engineering
Subject area: Electrical Engineering
Version: 5
Applies from: 2013-11-20
Approved: 2013-11-20
Replaces course syllabus approved: 2009-11-01
1 Course title and credit points
The course is titled Adaptive Signal
Processing/Adaptiv signalbehandling and awards
7,5 ECTS credits. One credit point (högskolepoäng)
corresponds to one credit point in the European
Credit Transfer System (ECTS).
2 Decision and approval
This course is established by Department for
Electrical Engineering 2013-11-20. The course
syllabus was revised by School of Engineering and
applies from 2013-11-20.
Reg.no: BTH 4.1.1-0818-2013.
Replaces ET2432.
3 Objectives
The student will acquire the background to and
knowledge of adaptive and optimal systems. The
student will also acquire insights into and
experiences of applied signal processing problems
of which these systems form part.
4 Content
Central items of the course are:
Stochastic signals
Discrete stochastic processes, correlation, spectral
density, cross spectrum, models for stochastic
signals.
Optimal signal processing
Estimation, least square error, the normal equations,
least square filter, prediction, inverted filtration.
Adaptive signal processing
Introduction of the adaptive concept, iterative
solution, the adaptive LMS filter, stability,
convergence. Applications such as noise reduction,
signal improvement and echo extinction.
Algorithms, variants of the LMS.
Laboratory work
Software based laboratory work.
5 Aims and learning outcomes
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On completion of the course the student will be able
to:
•design and implement the Wiener filter
•recognize situations where adaptive systems may
provide a good solution
6 Generic skills
The following generic skills are trained in the
course:
• Capacity for applying knowledge in practice.
• Capacity for analysis and synthesis
• General knowledge in the subject area of the
studies.
7 Learning and teaching
The teaching consists of lectures, exercises, home
assignments, and laboratory work. The home
assignments are compulsory and must be done
individually. During arithmetical problems the
exercise instructor illustrates how the theory that
has been learnt should be applied on signal
processing problems. In order to further explain the
theory and its applications there is a compulsory
laboratory work. The laboratory work may be
carried out individually or in groups.
The teaching language is English.
The teaching language is English.
8 Assessment and grading
Examination of the course
-------------------------------------------------
Code Module Credit Grade
-------------------------------------------------
Written examination[1] 6 ECTS A-F
Laboratory work + written assignment 1.5 ECTS
G-U
-------------------------------------------------
1 Determines the final grade for the course, which
will only be issued when all components have been
approved.
The course will be graded A Excellent, B Very good,
C Good, D Satisfactory, E Sufficient, FX Insufficient,
supplementation required, F Fail.If grade FX or UX
2. are given, the student may after consultation with
the course coordinator / examiner get an
opportunity to within 6 weeks complement to grade
E or G for the specific course element.
The examination is done through a written exam
together with an account of the compulsory home
assignments, and the laboratory work assignments.
Grading of the laboratory work assignments is done
with the grades Pass or Fail, with the grade of Pass
required for obtaining a final grade of the course.
This final grade will be the same as the examination
grade.
9 Course evaluation
The course coordinator is responsible for
systematically gathering feedback from the students
in course evaluations and making sure that the
results of these feed back into the development of
the course.
10 Prerequisites
Required courses for admission to this course:
ET1303 Signal Processing II and
MS1101 Mathematical Statistics
11 Field of education and subject area
The course is part of the field of education and is
included in the subject area Electrical Engineering.
12 Restrictions regarding degree
The course cannot form part of a degree with
another course, the content of which completely or
partly corresponds with the contents of this course.
13 Additional information
The course MS1102 Stochastic Processes is
recommended as previous knowledge but does not
constitute a formal requirement.
14 Course literature and other teaching material
Monson H. Hayes Statistical Digital Signal Processing
and Modeling, Wiley 1996. ISBN 0-471-59431-8.
Material from the department.
s
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